Abstract
A local refinement method for data partitioning has been constructed. The method balances the workload and minimizes locally the number of edge-cuts. The arithmetic complexity of the algorithm is low. The method is well suited for refinement in multilevel partitioning where the intermediate partitions are near optimal but slightly unbalanced. It is also useful for improvement of global partitioning methods and repartitioning in dynamic problems where the workload changes slightly. The algorithm has been compared with corresponding methods in Chaco and Metis in the context of multilevel partitioning. The cost of carrying out the partitioning with our method is lower than in Chaco and of the same order as in Metis, and still the quality of the partitioning is comparable and in some cases even better.
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Rantakokko, J. (2001). A Local Refinement Algorithm for Data Partitioning. In: Sørevik, T., Manne, F., Gebremedhin, A.H., Moe, R. (eds) Applied Parallel Computing. New Paradigms for HPC in Industry and Academia. PARA 2000. Lecture Notes in Computer Science, vol 1947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-70734-4_18
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DOI: https://doi.org/10.1007/3-540-70734-4_18
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